Abstract

The purpose of this thesis was to develop further the Learning Index in perimetry and examine how it performs in different groups, with different algorithms and investigate different procedures of calculation.
The Learning Index calculated using concentric rings of visual field data, following the method of Olsson and colleagues (1997), facilitated in a MatLab environment. The used data included visual field assessment for 29 normal, 25 glaucoma and 25 ocular hypertensive individuals who followed perimetry for both eyes, for different strategies and for five consecutive visits once a week.
Alternative methods to evaluate the LI were used like the glaucoma hemifield test pattern. The influence of the different strengths of a variety of filters was also used, filtering the perimeter outcome in order to disassociate learning effect from real defects. Mean and Median filters were also used, and dissimilar Adaptive filters as well, that seemed to be robust filters that could help to establish a more sensitive Learning Index.
In automated perimetry the innovation of a Learning Index would consider and examine how individuals learn to perform better visual field tests during recurrent visits under different algorithms. The argument is if that Learning Index could allow clinicians performing visual field tests to administer their patients and control possible detected abnormality, after their first or second visual field test. In this way they will prevent development of the disease, confine patient’s fatigue and provide quality of life and simultaneously financial savings for the state and private health organizations.
The carried out learning index calculations results were sufficiently encouraging for a next phase of a future index development and with likelihood in the future to be incorporated in automated perimeters algorithms.